Harnessing the Influence of Pressure and Nutrients on Biological CO<sub>2</sub> Methanation Using Response Surface Methodology and Artificial Neural Network—Genetic Algorithm Approaches
The biological methanation process has emerged as a promising alternative to thermo-catalytic methods due to its ability to operate under milder conditions. However, challenges such as low hydrogen solubility and the need for precise trace element supplementation (Fe(II), Ni(II), Co(II)) constrain m...
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2025-01-01
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| author | Alexandros Chatzis Konstantinos N. Kontogiannopoulos Nikolaos Dimitrakakis Anastasios Zouboulis Panagiotis G. Kougias |
| author_facet | Alexandros Chatzis Konstantinos N. Kontogiannopoulos Nikolaos Dimitrakakis Anastasios Zouboulis Panagiotis G. Kougias |
| author_sort | Alexandros Chatzis |
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| description | The biological methanation process has emerged as a promising alternative to thermo-catalytic methods due to its ability to operate under milder conditions. However, challenges such as low hydrogen solubility and the need for precise trace element supplementation (Fe(II), Ni(II), Co(II)) constrain methane production yield. This study investigates the combined effects of trace element concentrations and applied pressure on biological methanation, addressing their synergistic interactions. Using a face-centered composite design, batch mode experiments were conducted to optimize methane production. Response Surface Methodology (RSM) and Artificial Neural Network (ANN)—Genetic Algorithm (GA) approaches were employed to model and optimize the process. RSM identified optimal ranges for trace elements and pressure, while ANN-GA demonstrated superior predictive accuracy, capturing nonlinear relationships with a high R² (>0.99) and minimal prediction errors. ANN-GA optimization indicated 97.9% methane production efficiency with a reduced conversion time of 15.9 h under conditions of 1.5 bar pressure and trace metal concentrations of 25.0 mg/L Fe(II), 0.20 mg/L Ni(II), and 0.02 mg/L Co(II). Validation experiments confirmed these predictions with deviations below 5%, underscoring the robustness of the models. The results highlight the synergistic effects of pressure and trace metals in enhancing gas–liquid mass transfer and enzymatic pathways, demonstrating the potential of computational modeling and experimental validation to optimize biological methanation systems, contributing to sustainable methane production. |
| format | Article |
| id | doaj-art-1df0b2be750a48dea4e128d3ae2a38f4 |
| institution | Kabale University |
| issn | 2311-5637 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Fermentation |
| spelling | doaj-art-1df0b2be750a48dea4e128d3ae2a38f42025-01-24T13:32:10ZengMDPI AGFermentation2311-56372025-01-011114310.3390/fermentation11010043Harnessing the Influence of Pressure and Nutrients on Biological CO<sub>2</sub> Methanation Using Response Surface Methodology and Artificial Neural Network—Genetic Algorithm ApproachesAlexandros Chatzis0Konstantinos N. Kontogiannopoulos1Nikolaos Dimitrakakis2Anastasios Zouboulis3Panagiotis G. Kougias4Laboratory of Chemical and Environmental Technology, Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceSoil and Water Resources Institute, Hellenic Agricultural Organisation Dimitra, Thermi, 57001 Thessaloniki, GreeceWyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02215, USALaboratory of Chemical and Environmental Technology, Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceSoil and Water Resources Institute, Hellenic Agricultural Organisation Dimitra, Thermi, 57001 Thessaloniki, GreeceThe biological methanation process has emerged as a promising alternative to thermo-catalytic methods due to its ability to operate under milder conditions. However, challenges such as low hydrogen solubility and the need for precise trace element supplementation (Fe(II), Ni(II), Co(II)) constrain methane production yield. This study investigates the combined effects of trace element concentrations and applied pressure on biological methanation, addressing their synergistic interactions. Using a face-centered composite design, batch mode experiments were conducted to optimize methane production. Response Surface Methodology (RSM) and Artificial Neural Network (ANN)—Genetic Algorithm (GA) approaches were employed to model and optimize the process. RSM identified optimal ranges for trace elements and pressure, while ANN-GA demonstrated superior predictive accuracy, capturing nonlinear relationships with a high R² (>0.99) and minimal prediction errors. ANN-GA optimization indicated 97.9% methane production efficiency with a reduced conversion time of 15.9 h under conditions of 1.5 bar pressure and trace metal concentrations of 25.0 mg/L Fe(II), 0.20 mg/L Ni(II), and 0.02 mg/L Co(II). Validation experiments confirmed these predictions with deviations below 5%, underscoring the robustness of the models. The results highlight the synergistic effects of pressure and trace metals in enhancing gas–liquid mass transfer and enzymatic pathways, demonstrating the potential of computational modeling and experimental validation to optimize biological methanation systems, contributing to sustainable methane production.https://www.mdpi.com/2311-5637/11/1/43biomethanationtrace elementsCO<sub>2</sub> utilizationoptimizationmachine learning |
| spellingShingle | Alexandros Chatzis Konstantinos N. Kontogiannopoulos Nikolaos Dimitrakakis Anastasios Zouboulis Panagiotis G. Kougias Harnessing the Influence of Pressure and Nutrients on Biological CO<sub>2</sub> Methanation Using Response Surface Methodology and Artificial Neural Network—Genetic Algorithm Approaches Fermentation biomethanation trace elements CO<sub>2</sub> utilization optimization machine learning |
| title | Harnessing the Influence of Pressure and Nutrients on Biological CO<sub>2</sub> Methanation Using Response Surface Methodology and Artificial Neural Network—Genetic Algorithm Approaches |
| title_full | Harnessing the Influence of Pressure and Nutrients on Biological CO<sub>2</sub> Methanation Using Response Surface Methodology and Artificial Neural Network—Genetic Algorithm Approaches |
| title_fullStr | Harnessing the Influence of Pressure and Nutrients on Biological CO<sub>2</sub> Methanation Using Response Surface Methodology and Artificial Neural Network—Genetic Algorithm Approaches |
| title_full_unstemmed | Harnessing the Influence of Pressure and Nutrients on Biological CO<sub>2</sub> Methanation Using Response Surface Methodology and Artificial Neural Network—Genetic Algorithm Approaches |
| title_short | Harnessing the Influence of Pressure and Nutrients on Biological CO<sub>2</sub> Methanation Using Response Surface Methodology and Artificial Neural Network—Genetic Algorithm Approaches |
| title_sort | harnessing the influence of pressure and nutrients on biological co sub 2 sub methanation using response surface methodology and artificial neural network genetic algorithm approaches |
| topic | biomethanation trace elements CO<sub>2</sub> utilization optimization machine learning |
| url | https://www.mdpi.com/2311-5637/11/1/43 |
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